Under conventional approaches, training data (e.g., LIDAR data, images) may include observations of objects in real environments. For example, training data may include observations of vehicles on the road. However, limiting training data to real-observations of objects may result in the training data not including information on certain events, such as rare/abnormal events. For example, if a likelihood of a particular event relating to a vehicle occurring in real life is very low, then observations of the vehicle for a limited amount of time may not include an observation of the particular event. Tools that are configured using training data missing observations of events may not be prepared to deal with occurrences of unobserved events.